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Towards a distributed connectionist account of cognates and interlingual homographs: evidence from semantic relatedness tasks.

ABSTRACT: Background:Current models of how bilinguals process cognates (e.g., "wolf", which has the same meaning in Dutch and English) and interlingual homographs (e.g., "angel", meaning "insect's sting" in Dutch) are based primarily on data from lexical decision tasks. A major drawback of such tasks is that it is difficult-if not impossible-to separate processes that occur during decision making (e.g., response competition) from processes that take place in the lexicon (e.g., lateral inhibition). Instead, we conducted two English semantic relatedness judgement experiments. Methods:In Experiment 1, highly proficient Dutch-English bilinguals (N = 29) and English monolinguals (N = 30) judged the semantic relatedness of word pairs that included a cognate (e.g., "wolf"-"howl"; n = 50), an interlingual homograph (e.g., "angel"-"heaven"; n = 50) or an English control word (e.g., "carrot"-"vegetable"; n = 50). In Experiment 2, another group of highly proficient Dutch-English bilinguals (N = 101) read sentences in Dutch that contained one of those cognates, interlingual homographs or the Dutch translation of one of the English control words (e.g., "wortel" for "carrot") approximately 15 minutes prior to completing the English semantic relatedness task. Results:In Experiment 1, there was an interlingual homograph inhibition effect of 39 ms only for the bilinguals, but no evidence for a cognate facilitation effect. Experiment 2 replicated these findings and also revealed that cross-lingual long-term priming had an opposite effect on the cognates and interlingual homographs: recent experience with a cognate in Dutch speeded processing of those items 15 minutes later in English but slowed processing of interlingual homographs. However, these priming effects were smaller than previously observed using a lexical decision task. Conclusion:After comparing our results to studies in both the bilingual and monolingual domain, we argue that bilinguals appear to process cognates and interlingual homographs as monolinguals process polysemes and homonyms, respectively. In the monolingual domain, processing of such words is best modelled using distributed connectionist frameworks. We conclude that it is necessary to explore the viability of such a model for the bilingual case. Data scripts materials and pre-registrations:Experiment 1: http://www.osf.io/ndb7p; Experiment 2: http://www.osf.io/2at49.

SUBMITTER: Poort ED

PROVIDER: S-EPMC6526012 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Towards a distributed connectionist account of cognates and interlingual homographs: evidence from semantic relatedness tasks.

Poort Eva D ED Rodd Jennifer M JM

PeerJ 20190516

<h4>Background</h4>Current models of how bilinguals process cognates (e.g., "wolf", which has the same meaning in Dutch and English) and interlingual homographs (e.g., "angel", meaning "insect's sting" in Dutch) are based primarily on data from lexical decision tasks. A major drawback of such tasks is that it is difficult-if not impossible-to separate processes that occur during decision making (e.g., response competition) from processes that take place in the lexicon (e.g., lateral inhibition). ...[more]

PMID: 31143528

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Project description:BackgroundComputing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a 'closed' problem.ResultsWe propose a method in which computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts. As keyterm extraction is a costly process, it is done in a preprocessing step on a 'per-document' basis in order to limit the on-line processing. The actual computations are executed in a compact vector space, limited by the most informative extraction results. The method has been evaluated on five direct benchmarks by calculating correlation coefficients w.r.t. average human answers. It also has been used on Gene - Disease and Disease- Disease data pairs to highlight its potential use as a data analysis tool. Apart from comparisons with reported results, some interesting features of the method have been studied, i.e. the relationship between result quality, efficiency and applicable trimming threshold for size reduction. Experimental evaluation shows that the presented method obtains results that are comparable with current state of the art methods, even surpassing them on a majority of the benchmarks. Additionally, a possible usage scenario for the method is showcased with a real-world data experiment.ConclusionsOur method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources.

Dataset Information

Towards a distributed connectionist account of cognates and interlingual homographs: evidence from semantic relatedness tasks.

Publications

Towards a distributed connectionist account of cognates and interlingual homographs: evidence from semantic relatedness tasks.

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